Author Description

In-silico calculation of binding free energy between protein and ligands has vast applications in the initial stages of drug discovery. Most of the classical physics-based models, including implicit solvents, ignore entropy contributions from the system. Instead, a simplified solvent entropy is indirectly considered. This simplification is often done because of an under-sampled conformal space due to physics calculation complexity. Machine learning (ML) methods offer a practical venue to incorporate accurate binding entropy predictions from the experiment. While accurate, there are growing concerns about the overfitting of ML models to the training set, lack of interpretation due to its 'black box' characteristics, and failure to comply with well-known physical models. Recently emerged, physics-guided models are a class of ML models that combine the robust consistency of physics-based models with the accuracy of modern data-driven algorithms. This work presents a method to design two hybrid models by coupling ML with a physics model. Implementing these hybrid models has been done through careful modification of various model learning parameters or hyperparameters. The proposed hybrid models outperform purely data-driven models by at least 10%. We review the basic theory, investigate binding entropy calculation methods, present hybrid models that take advantage of end-point simulation software, and analyze the performance of these models.